Cybersecurity Situation Assessment Model Based on the Dynamic Fuzzy Analytic Hierarchy Process Tree (D-Fahp Tree)

In the face of increasingly complex and dynamic cyber threats, traditional static assessment models are inadequate for supporting real-time and adaptive cybersecurity risk analysis. This paper proposes a novel cybersecurity situation assessment model based on the Dynamic Fuzzy Analytic Hierarchy Process Tree (D-FAHP Tree), designed to quantitatively evaluate security posture with improved sensitivity, adaptability, and real-time responsiveness. The proposed model integrates fuzzy evaluation methods and hierarchical analysis within a closedloop structure that supports continuous weight adjustment based on attack- driven feedback. Unlike conventional models with fixed weights, the D-FAHP Tree dynamically adapts to threat evolution by introducing an event-response feedback mechanism, including frequency, severity, and coverage-based impact factors. Additionally, an iterative optimization process is formulated to refine indicator weights in response to observed security incidents, enabling the model to effectively reflect the current threat landscape. Experimental results using real-world and simulated attack datasets demonstrate that the D-FAHP Tree significantly en- hances situational awareness accuracy and convergence stability compared to static models. The model's flexible hierarchical structure also supports expansion across multiple security do- mains, including intrusion prevention, application protection, behavior monitoring, and data integrity. This study offers a theoretical and practical foundation for intelligent cybersecurity assessment, contributing to the development of proactive and resilient security operations frameworks.